weka machine learning

Turn from 70271574AI (AI) is the future, is science fiction, is part of our daily life. All the assertions are correct, just to see what you are talking about AI in the end.For example, when Google DeepMind developed the Alphago program to defeat the Korean professional Weiqi master Lee Se-dol, the media in the description of the victory of DeepMind used AI, machine learning, deep

perhaps this loss function is quite in line with the characteristics of SVM ~Multi-Classification problemMethod One:As shown--each time a category is taken out, other categories are synthesized into a large category, which is treated as a two classification problem. Repeat n times to be OKCons: The category of the line will be biased to the training data of the smaller categoryMethod Two: Simultaneous requestExplain the formula:The left is a point of classification at J XJ multiplied by its own

Machine Learning-multiple linear regression and machine Linear Regression What is multivariate linear regression? In linear regression analysis, if there are two or more independent variablesMultivariable linear regression). If we want to predict the price of a house, the factors that affect the price may include area, number of bedrooms, number of floors, and ag

The stronger the fault tolerance, the better.B is the plane's biased forward, W is the plane's normal vector, and the X-to-plane mapping:First of all, the point is the smallest distance from the dividing line, and then ask what kind of W and B, so that the point, the value of the distance dividing line is the largest.After shrinking:and taking it as min, take yi* (W^t*q (xi) + b) = 1 =Machine Learning algor

above question, we can apply the kernel function:Quadratic coefficient q n,m = y n y m z n T z m = y n y m K (x N, x m) to get the Matrix Qd.So, we need not to de the caculation in space of Z, but we could use KERNEL FUNCTION to get znt*zm used xn and XM.Kernel Trick:plug in efficient Kernel function to avoid dependence on d?So if we give the This method a name called Kernel SVM:Let us come back to the 2nd polynomial, if we add some factor into expansion equation, we may get some new kernel fun

large enough to allocate more, for learning to use 20G is enough, there is no tick "allocate all disk space immediately", tick, will immediately allocate 20G from the host disk to the physical machine. Select Save Virtual Disk as a single file, next.650) this.width=650; "Src=" Https://s3.51cto.com/oss/201711/17/9876dd45416d827e0766eb946dae21b8.png-wh_500x0-wm_3 -wmp_4-s_1109685317.png "title=" Linux virtua

take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three algorithms, we

, simulated annealing algorithm), GA (Genetic algorithm genetic algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).

is still published as a reading note, not involving too many code and tools, as an understanding of the article to introduce machine learning.The article is divided into two parts, machine learning Overview and Scikit-learn Brief Introduction, the two parts of close relationship, combined writing, so that the overall length, divided into 1, 22.First, it's about

gradient descent algorithm: linear regression Model:　　　　　　　　　　　　　　Linear hypothesis:Squared difference cost function:By substituting each formula, the θ0 and θ1 are respectively biased:By substituting the partial derivative into the gradient descent algorithm, we can realize the process of finding the local optimal solution.The cost function of linear regression is always a convex function, so the gradient descent algorithm only has a minimum value after execution." Batch " gradient descent: use

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on